Individual Risk Attitudes:Measurement, Determinants and Behavioral
Consequences
Thomas Dohmen1, Armin Falk2, David Huffman1, Uwe Sunde3
Forthcoming in the Journal of The European Economic Association
Abstract
This paper studies risk attitudes using a large representative survey and also acomplementary field experiment based on a representative subject pool. Using aquestion asking people about their willingness to take risks ”in general”, we findthat gender, age, height, and parental background have an economically significantimpact on willingness to take risks. The field experiment confirms the behavioralvalidity of this measure, using paid lottery choices. Turning to other questions aboutrisk attitudes in specific contexts, we find similar results on the determinants of riskattitudes, and also shed light on the deeper question of stability of risk attitudesacross contexts. We conduct a horse race of the ability of different measures toexplain risky behaviors such as holdings stocks, occupational choice, and smoking.The question about risk-taking in general generates the best all-around predictor ofrisky behavior.
(JEL codes: D0, D1 D80, D81, C91, C93, J16, J24, I1)
Risk and uncertainty play a role in almost every important economic decision. As
a consequence, understanding individual attitudes towards risk is intimately linked to
the goal of understanding and predicting economic behavior. A growing literature has
made progress on developing empirical measures of individual risk attitudes, with the
aim of capturing this important component of individual heterogeneity (see, e.g., Bruhin,
Fehr-Duda and Epper, 2007), but many questions remain unresolved.
One important open question concerns the determinants of individual differences in
risk attitudes. Previous studies have measured risk attitudes using survey questions, and
found mixed evidence on determinants, for example gender.1 A second open question,
however, is whether survey questions are really a good method for measuring risk atti-
tudes. Because survey questions are not incentive compatible, economists are skeptical
1 See, for example, Barsky et al. (1997), Guiso and Paiella (2001) 2005, Donkers et al. (2001), Guiso etal. (2002), Diaz-Serrano and O’Neill (2004).
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about whether self-reported personal attitudes and traits are behaviorally meaningful.
Various factors, including self-serving biases, inattention, and strategic motives could
cause respondents to distort their reported risk attitudes (for a discussion, see Camerer
and Hogarth, 1999). Experimental studies, which measure risk-taking behavior with real
money at stake, on the other hand, offer an incentive compatible measure of risk atti-
tudes.2 However, a drawback of this technique is that it is costly and difficult to perform
with a large, representative sample.
Another largely unexplored issue is how context and question format matter for
eliciting risk attitudes. Survey studies have almost always used a question framed in one
relatively specific context: a (hypothetical) decision regarding a financial lottery. It is
desirable, however, to include multiple questions about risk attitudes in the same survey,
using different contexts and approaches. This is important for assessing the robustness
of results such as the gender effect to the use of different types of risk measures, and it
is also useful for determining the best way to reliably capture an individual’s risk taking
tendencies in different contexts.
This paper uses two different sources of data in order to make progress on this col-
lection of questions. The first data source is the German Socio-Economic Panel (SOEP),
which measures the risk attitudes of more than 22,000 individuals. The sample is care-
fully constructed to be representative of the adult population living in Germany. The
representativeness and statistical power afforded by the survey allow us to study the de-
terminants of risk attitudes in detail. The survey is also attractive in that it incorporates
a variety of different approaches to measuring risk attitudes. For example, one question
directly asks individual’s to make a global assessment of their willingness to take risks:
“How willing are you to take risks, in general?” Respondents rate their willingness on a
scale from 0 to 10. We call this simple, qualitative measure the “general risk question”.
Initially, we use this measure to study heterogeneity and determinants of risk attitudes
in the population. Our first set of results includes evidence of substantial heterogeneity
2 See, e.g., Schubert et al. (1999), Holt and Laury (2002), Barr and Packard (2002), Eckel et al. (2005),and Eckel and Grossman (2007).
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in risk attitudes, and strong evidence that gender, age, height, and parental background
play an important role in explaining individual differences in risk attitudes.
A crucial concern is whether survey questions can be meaningfully interpreted in
terms of actual risk-taking behavior. In order to address this question, we use a sec-
ond data source: a field experiment conducted with an additional representative sample
of 450 subjects, drawn from the adult population in Germany using exactly the same
methodology as for the SOEP. Participants in our experiment answer the same general
risk question asked to participants in the SOEP. The respondents also make choices in a
real-stakes lottery experiment. We show that responses to the general risk question are
in fact a reliable predictor of actual risky behavior, even controlling for a large number of
observables. This allows confidence in the behavioral validity of our results for the larger
survey data set, based on the validated survey measure. More generally, the findings show
that this simple, qualitative survey measure can generate a meaningful measure of risk
attitudes, which maps into actual choices in lotteries with real monetary consequences.
This is important because economists are typically skeptical about the value of survey
measures, and because a simple and behaviorally valid measure of risk attitudes is very
useful for the many areas of economic research relying on survey data.
Having established the behavioral validity of the general risk question in the field
experiment, we return to an analysis of the larger SOEP data set, and exploit additional
questions about risk attitudes. In particular, the survey includes five additional questions
that use the same scale as the general risk question, but ask about risk taking in specific
contexts: car driving, financial matters, sports and leisure, health, and career. We find
that the results on determinants are robust to using these alternative risk measures, e.g.,
females are less willing to take risks in every context. We also assess the stability of
risk attitudes across contexts. In economics it is common to think of a single trait as
governing risk-taking in all contexts, whereas in psychology there is more controversy on
this point.3 We find that risk attitudes are strongly but not perfectly correlated across
contexts. This suggests the presence of a common underlying risk trait, but also points
3 See, e.g., Slovic (1972a).
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to some value-added from asking context-specific questions.
Finally, the survey includes self-reported information on several important risky be-
haviors: holding stocks, being self-employed, participating in sports, and smoking. This
allows us to conduct a horse race with all of our measures of risk attitudes, judging
their relative abilities to explain these risky behaviors. We find that all measures are
significantly related to several behaviors, providing further evidence on their behavioral
validity. The best all-around explanatory variable, however, is the general risk question,
which predicts all behaviors. On the other hand, although less successful across contexts,
the single best risk measure in any given context is the measure incorporating the cor-
responding specific context. For example, the best predictor of smoking is the question
about willingness to take risks in health matters, rather than the general risk question
or questions incorporating different contexts. These findings indicate that asking for a
global assessment of willingness to take risks in fact induces respondents to supply a use-
ful all-around measure. Questions focused on specific contexts do less well as all-around
predictors, but provide strong measures within their particular domain of risky behavior.
The organization of the paper is as follows. Section 1 describes the SOEP and
the risk measures that we use. Section 2 investigates heterogeneity and determinants
of risk attitudes using the general risk question. Section 3 presents results from the
complementary field experiment. Section 4 assesses the stability of risk attitudes across
different contexts. Section 5 compares the predictive power of the different risk measures
and Section 6 discusses the implications of our results.
1 Data Description
The SOEP is a representative panel survey of the resident adult population of Germany
(for a detailed description, see Wagner et al., 1993, and Schupp and Wagner, 2002).
The initial wave of the survey was conducted in 1984.4 The SOEP surveys the head
4 The panel was extended to include East Germany in 1990, after reunification. For more details on theSOEP, see www.diw.de/gsoep/.
4
of each household in the sample, but also gives the full survey to all other household
members over the age of 17. Respondents are asked for a wide range of personal and
household information, and for their attitudes on assorted topics, including political and
social issues. Our analysis uses the 2004 wave, which includes 22,019 individuals in 11,803
different households.
Much of our analysis focuses on the general risk question in the SOEP, which directly
asks respondents to give a global assessment of their willingness to take risks. The exact
wording of which (translated from German) is as follows: “How do you see yourself: are
you generally a person who is fully prepared to take risks or do you try to avoid taking
risks? Please tick a box on the scale, where the value 0 means: ‘not at all willing to
take risks’ and the value 10 means: ‘very willing to take risks’.”5 Notably, the measure
is qualitative and does not involve an explicit lottery. Rather, it relies on the subject
to give an assessment of willingness to take risks in general, across the various types of
lotteries (some of which may be non-financial) that could be faced in decision making.
This approach is potentially attractive, for the purpose of eliciting a reliable all-around
measure of risk attitudes across contexts, something which we evaluate empirically in our
analysis. Because there are no explicit stakes or probabilities in the question, there is the
potential that factors other than risk preference could lead to variation in responses across
individuals. Specifically, subjective beliefs about the riskiness of the decision environment
could affect someone’s stated willingness to take risks. For this reason, it is informative
whether the measure explains risky behavior in our field experiment, where choices involve
paid lotteries with explicit stakes and probabilities, and thus subjective beliefs about risk
are held constant. A positive result from the validation exercise would confirm that the
measure does not just reflect subjective beliefs. Five additional measures use the same
wording as the general risk question, and the same scale, but ask about willingness to
take risks in a specific context: car driving, financial matters, leisure and sports, career,
5 In German, the wording of the question is: “Wie schatzen Sie sich personlich ein: Sind Sie im all-gemeinen ein risikobereiter Mensch oder versuchen Sie, Risiken zu vermeiden? Bitte kreuzen Sie einKastchen auf der Skala an, wobei der Wert 0 bedeutet: “gar nicht risikobereit” und der Wert 10: “Sehrrisikobereit”. Mit den Werten dazwischen konnen Sie Ihre Einschatzung abstufen.”
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and health.6
Not every respondent answered all questions, but non-response rates are fairly low.
The number of non-responses out of 22,019 for each of the six question is as follows: 72
for general, 1,349 for car driving, 262 for financial matters, 379 for sports and leisure,
2,051 for career, and 85 for health.
Our analysis also incorporates a field experiment, in which the subjects are a random
sample of the population, drawn using exactly the same procedure as for the SOEP
(sampling is done using the targeted random walk method; see Thompson, 2006).7 The
experiment involved a separate subject pool, rather than involving participants from the
SOEP panel. In the experiment, subjects answered a questionnaire similar to the SOEP,
which included the general risk question.8 Subjects then took part in a lottery experiment.
We describe the experiment in detail in the validation section below. The questionnaire
and experiment were conducted by experienced interviewers who were recruited out of the
pool of interviewers who conduct the regular SOEP survey. Interviews took place face-to-
face at the subjects’ homes. Both answers to the questionnaire and the decisions in the
lottery experiment were typed into a computer (Computer Assisted Personal Interview
(CAPI)). The study was run between June 9th and July 4th, 2005, and a total of 450
participants took part.
2 Willingness to Take Risks in General
This section presents the distribution of willingness to take risks in the population, as
measured by the general risk question, and then turns to the investigation of possible
determinants of individual differences in risk attitudes.
6 German versions of all risk questions are available online, at www.diw.de/gsoep/.7 For each of 179 randomly chosen primary sampling units (voting districts), one trained interviewer
was given a randomly chosen starting address. Starting at that specific local address, the interviewercontacted every third household and interviewed one adult person aged 16 or older per household.
8 There was a zero non-response rate for the general risk question in the experimental study.
6
2.1 Heterogeneity and exogenous determinants
The upper panel of Figure 1 shows the distribution of general risk attitudes in our repre-
sentative sample. Each bar indicates the fraction of individuals choosing a given number
on the eleven point risk scale. The figure reveals substantial heterogeneity in risk atti-
tudes across the population: the modal response is 5, but risk attitudes vary widely over
the entire scale, with mass distributed over the entire support. A relatively small fraction
of respondents chooses a value of 10, indicating that they are very willing to take risks,
while a somewhat larger mass, roughly 7 percent of all individuals, choose 0, indicating
that they are not at all willing to take risks.
Next, we investigate whether some of the heterogeneity in risk attitudes is system-
atic, thus leading to differences in economic decisions across different types of individuals.
We focus on the impact of four personal characteristics: gender, age, height, and parental
background. These characteristics are plausibly exogenous with respect to individual risk
attitudes and behavior, and thus allow us to give a causal interpretation to correlations
and regression results.9 There are also important implications if these characteristics have
an impact on risk attitudes. For example, a gender difference in risk attitudes could be
part of the explanation for gender differences in social behavior and economic outcomes.
The lower panel of Figure 1 shows the difference between the fraction of women and
the fraction of men choosing each value on the general risk scale. Clearly, women are
more likely to choose low values on the scale than men, and men are more likely than
women to choose high values. The figure thus gives an initial indication that women are
less willing to take risks than men. Figure 2 displays the relationship between age and
risk attitudes. For each age, the shading indicates the fraction of individuals who choose
a given value on the risk scale. Clearly, the proportion of individuals who are relatively
unwilling to take risks, i.e., choose low values on the scale, increases strongly with age.
Comparing the panels for men and women, it appears that the age effect is relevant for
both sexes, and that women are less willing to take risks than men at all ages, although
9 Note, however, the caveat that age could potentially be endogenous, for example if people who are lesswilling to take risks live longer.
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the gap narrows somewhat among the elderly.10
Figure 3 gives an indication that family background plays a role in determining risk
attitudes. The histograms show the distribution of responses to the general risk question
by parental education. As a proxy for highly-educated parents, we use information on
whether or not a parent passed the Abitur, an exam that comes at the end of university-
track high school in Germany and is a prerequisite for attending university.11 The mass
in the histograms for individuals with highly-educated parents, shown in the bottom
panel, appears shifted to the right compared to histograms for individuals without highly-
educated parents, shown in the upper panel, indicating a positive correlation between
parental education and willingness to take risks. Figure 4 presents histograms of responses
to the general risk question by height (self-reported), a characteristic that is directly
influenced by an individual’s parents.12 The two panels show separate results for men
and women. For both genders, taller individuals are more willing to take risks.
2.2 The Joint Role of Exogenous Determinants
To determine whether the unconditional results on determinants are robust once we con-
trol for all four exogenous characteristics simultaneously, we estimate regressions where
the dependent variable is an individual’s response to the general risk question. Because
the dependent variable is measured in intervals, on an 11-point scale, throughout the
analysis we use interval regression techniques.13 All estimation results report robust stan-
10 Willingness to take risks appears to decrease steadily with age for men, whereas for women willingnessto take risks decreases more rapidly from the late teens to age thirty, and then remains flat, until itbegins to decrease again from the mid-fifties onwards. Interestingly, this pattern in early life for womenis not solely driven by the occurrence of childbirth; the pattern is similar for the sub-sample of womenwho never have a child.
11 There are two types of high school in Germany, vocational and university-track. The Abitur is anexam that is completed at the end of university-track high schools and qualifies an individual to attenduniversity. Thus the Abitur is an indicator of relatively high academic achievement, especially for oldercohorts. In our sample roughly 7 percent of mothers and 13 percent of fathers have completed theAbitur.
12 This partly reflects genetic transmission, see, e.g., Tambs et al. (1992). Height is also influenced byparents through the channel of nutrition, and could partly reflect parental wealth. Height is frequentlyused as an instrument for child nutrition and health in the development literature see, e.g., Schultz(2002).
13 This approach treats each value of the dependent variable as a left and right censored observationcoming from an interval with known bounds and accounts for censoring. The interval regression
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dard errors, corrected for possible correlation of the error term across individuals from the
same household. The only sample restriction is the omission of individuals with missing
values for the variables in a given regression.
Table 1 summarizes our initial regressions. The baseline specification, presented in
column (1), uses gender, age and height as explanatory variables. The resulting coefficient
estimates show that the unconditional results remain robust. Women are significantly less
willing to take risks in general. Willingness to take risks also decreases significantly with
age. Unreported regressions including age in splines, with knots at 30 and 60 years,
reveal that the age effect is particularly strong for young and old ages, consistent with
the patterns displayed in Figure 2. The inclusion of splines leaves the estimates of the
other coefficients virtually unchanged.14 Taller people are more willing to take risks. All
of these effects are individually and jointly significant at the 1-percent level.15 Column
(2) repeats the same estimation adding education of mother and father as exogenous
characteristic from the individual point of view. Having a mother or father who is highly
educated significantly increases willingness to take risks. Again, this effect is individually
and jointly significant at any conventional level.
Columns (3) to (6) check the robustness of our findings by including other control
variables. Two potentially important controls are income and wealth. High income or
wealth levels may increase the willingness to take risks because they cushion the impact
of bad realizations. A potential problem with adding these variables to the regression is
that they may be endogenous, e.g., a greater willingness to take risks could lead to high
wealth levels. Wealth and income are sufficiently important economic variables, however,
procedure maximizes a likelihood function that is a natural generalization of a Tobit. Error terms areassumed to be normally distributed. We also estimated all regressions using Ordered Probit models,OLS regressions, or using a binary Probit classification with a measure of risk attitudes as the dependentvariable, which takes a value of 1 for individuals who report a value above 5 on the general risk scaleand 0 otherwise. In all instances we found the same qualitative results and similarly significant androbust coefficients. Results based on any of these alternative estimation methods are available uponrequest.
14 Results for spline regressions are available upon request.15 A likelihood-ratio test reveals that adding interaction terms between all independent variables improves
the fit. The coefficients of interest in the unrestricted specification, however, are very similar to thosefrom the restricted model, both qualitatively and quantitatively. We prefer the model reported incolumn (1) of Table 1 for ease of presentation and interpretation.
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that it is arguably important to know how they affect the baseline results when they
are included in the regression.16 Column (6) is the fullest specification, controlling for
household wealth and income simultaneously, and also adding a large number of other
personal and household characteristics. These additional characteristics, which are all
potentially endogenous, include among others: marital status, socialization in East or
West Germany, nationality, employment status (white collar, blue collar, private or public
sector, self-employed, non-participating), education, subjective health status, and religion.
For the sake of brevity, Table 1 does not report coefficient estimates for all of the additional
controls. The precise specification and all coefficient estimates are shown in Table A.1 in
the Appendix.
A comparison of the results in columns (3) to (6) to results in columns (1) and (2)
shows that the coefficient estimates for gender, age, and height are virtually unchanged,
and remain equally statistically significant when we include the additional income and
wealth controls. Women are less willing to take risks than men.17 Increasing age leads to
decreasing willingness to take risks, and increasing height leads to a greater willingness
to take risks. Having a mother who completed the Abitur, and somewhat less strongly
having a father who completed the Abitur, increases an individual’s willingness to take
risks.18
Importantly, the effects of gender, age, height, and parental education on willingness
to take risks are also quantitatively significant. For example, given that one standard
deviation for the general risk question is about 2.4, the gender effect corresponds to a
substantial decrease in willingness to take risks, about one quarter of a standard deviation.
In the last section of the paper, where we relate risk attitudes to different important risky
behaviors, we return to a discussion of the economic significance of the determinants
16 For details on the construction of income and wealth measures in the SOEP, see the notes for Table 1.17 We also studied the gender difference in risk attitudes using Oaxaca-Blinder decomposition techniques
(see Blinder, 1973, and Oaxaca, 1973), which allow the separation of differences in observable charac-teristics from differences in regression coefficients. We found that more than 60 percent of the gendergap is explained by differences in coefficients rather than characteristics, regardless of the specificationor the reference group chosen.
18 The fact that father’s education is less robust could reflect a correlation between father’s educationand children’s wealth, as well as the strong correlation between father’s education (and occupation)and children’s occupational choice.
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identified in this section.
Although causal interpretations are inadvisable, it is interesting that the correlation
between wealth or income and risk attitudes goes in the predicted direction, i.e., these
correlations are invariably positive and significant, indicating that wealthier individuals
are more willing to take risks. For example, the unconditional correlation between general
risk attitudes and log household income is 0.20, and the correlation with log household
wealth is 0.06, significant at any conventional level. The positive relationship between
income or wealth and willingness to take risks remains when we control for other observ-
ables (see coefficient estimates in column (1) of Table A.1 in the Appendix). There are
also a number of other noteworthy correlations, reported in Table A.1. For example, being
widowed, having a bad subjective health status, and being out of the labor force are all
significantly negatively correlated with willingness to take risks. Willingness to take risks
decreases with number of children. People with high life satisfaction are more willing to
take risks.
3 Experimental Validation of Survey Measures
The previous section identified several exogenous factors that determine individual risk
attitudes. Importantly, these conclusions were drawn from a large and representative
survey. The scope of the results is therefore considerably larger than that of economic
experiments, which typically use a relatively small and often selective sample. A serious
concern with the use of hypothetical questions, however, is that responses are not incentive
compatible. As a result it is unclear to what extent the general risk question is a reliable
indicator for actual risk taking behavior.
In light of this discussion, the researcher who is interested in the measurement of risk
attitudes faces a dilemma. Running an incentive compatible experiment with, say, 22,000
subjects is hardly a feasible option, given the substantial associated administrative and
financial costs. Conducting experiments with affordable but relatively small sample sizes,
on the other hand, leaves the researcher with limited statistical power. In this paper our
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solution is to run a large survey including risk measures but also a complementary field
experiment that tests the behavioral validity of the survey measures. This procedure offers
the advantages of both statistical power and confidence in the reliability of the survey
questions.19 In order to validate our survey risk measure, we ran a lottery experiment
based on a representative sample of adult individuals living in Germany, and had the same
individuals answer a detailed questionnaire. Of course, it would also be possible to validate
the measure in a lab experiment with undergraduates, a relatively easy and potentially
less expensive option. Strictly speaking, however, this would only allow validation of the
survey questions for this special subgroup of the total population, which is why we decided
on our alternative design.
In our experimental study, subjects first went through a detailed questionnaire, simi-
lar to the standard SOEP questionnaire. As part of the questionnaire we asked the general
risk question analyzed in the previous section. After completion of the questionnaire, par-
ticipants took part in a paid lottery experiment.20 In the experiment participants were
shown a table with 20 rows. In each row they had to decide whether they preferred a safe
option or playing a lottery. In the lottery they could win either 300 Euros or 0 Euros with
50 percent probability (1 Euro ∼ $ US 1.2 at the time of the experiment). In each row
the lottery was exactly the same but the safe option increased from row to row. In the
first row the safe option was 0 Euros, in the second it was 10 Euros, and so on up to 190
Euros in row 20. After a participant had made a decision for each row, it was randomly
determined which row became relevant for the participant’s payoff. Depending on the
subject’s choice in that row, the subject’s payoff would either be the safe payment from
19 There is a parallel with the literature on contingent valuation, which has used experiments to explorethe validity of survey questions about preferences for environmental amenities or other goods. See,e.g., Blackburn, et al. (1994), and Champ and Bishop (2001), among others.
20 The general risk question was asked as question 19 in the questionnaire, and the risk experiment wasconducted after subjects had answered question 104. Thus, a considerable amount of time (more than20 minutes) passed between answering the general risk question and making choices in the lotteryexperiment, during which participants answered 85 different questions, many with several sub-items.This time delay, together with the fact that the lottery choices involved considerable financial incentives,helps eliminate any spurious relationship between the general risk question and lottery choices, arisingdue to a psychological desire to make choices that are consistent with previous survey responses. Infact, see Saris and van Meurs (1990) and Saris (2003) for evidence that a time delay of 20 minutesbetween measures suffices to eliminate memory effects.
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that row, or the outcome of the lottery. This procedure guarantees that each decision
was incentive compatible. Once a respondent preferred the safe option to playing the
lottery, the interviewer asked whether the respondent would also prefer the even higher
safe payments to playing the lottery, and all subjects responded in the affirmative. The
switching point is informative about a subject’s risk attitude. Since the expected value
of the lottery is 150 Euros, weakly risk averse subjects should prefer safe options that are
smaller than or equal to 150 Euros over the lottery. Only risk loving subjects should opt
for the lottery when the offered safe option is 160, 170, 180, or 190 Euros.21
In order to ensure incentive compatibility, subjects were informed that after the
experiment a random device would determine whether they would be paid according to
their decision, and that the chance of winning was 1/7 (see Laury, 2006)), for evidence
that this delivers very similar results to the alternative procedure of paying each subject
with probability 1). At the end of the experiment subjects learnt the outcome of the
chance move, and in case they won they were paid by check sent to them by mail.22
Ideally, subjects who take part in the experiment should be as similar as possible to
the SOEP respondents, in particular with respect to the exogenous factors that explain
individual risk attitudes. We tried to make the samples as similar as possible by adopting
the exact same sampling methodology, and using the same surveying company as is used
for the SOEP (for detailed documentation see also Schupp and Wagner, 2007). As the
upper panel of Table 2 shows, the two samples are in fact quite similar. The fraction
of females is 52.7 percent in the experiment and 51.9 percent in the SOEP data. Also,
21 The increments in the safe payment, and maximum value of 190, were chosen to allow a relativelyfine grid for categorizing different degrees of risk aversion, risk neutrality, or risk lovingness, while alsokeeping the length of the choice table manageable. For example, safe payments higher than 190 wouldeither require lengthening the table, or using a coarser grid. As very few individuals are typicallyextremely risk loving, a maximum of 190 was chosen. As discussed below, the fact that the tableis bounded at 190 is inconsequential for our results. Only very few individuals are observed to beextremely risk loving, consistent with previous studies.
22 Sending checks is a particularly credible procedure in our case because the interviewers came from oneof Germany’s leading and most distinguished institutes in the field of social science survey research.Most people in Germany know this institute because it conducts major election polls that are reportedregularly on public broadcasting stations. It therefore enjoys a high reputation. In addition, it wouldhave been easy for respondents to contact the institute, because interviewers left their contact details aspart of the data protection protocol that household members signed and kept. None of the interviewersreported any credibility problems in their interviewer reports.
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both mean age and median age of the participants are very similar. The same holds for
height. This congruence reflects the representative character of the experimental subject
pool. Table 2 also shows that the mean and median response to the general risk question
is very similar. While the mean (median) value in the experiment is 4.76 (5), it is 4.42 (5)
for the people who are interviewed in the SOEP. In addition, the answers to the general
risk question are almost identically distributed (compare Figure 1 and Figure 5, upper
panel).23
The middle panel of Figure 5 shows a histogram of subjects’ choices in the experi-
ment. About 78 percent of the participants are risk averse. They prefer not to play the
lottery, which has an expected value of 150 Euros, when offered a safe payment smaller
than 150 Euros. About 13 percent are arguably risk neutral: 9 percent prefer a safe
payment of 150 Euros to the lottery, but play the lottery at smaller alternative options,
and 4 percent play the lottery when offered a safe payment equal to the expected value of
the lottery but do not play the lottery when the safe payment exceeds the expected value
of the lottery. About 9 percent of the subjects exhibit risk loving behavior, preferring the
lottery to safe amounts above 150 Euros. These proportions of risk averse, risk neutral,
and risk loving individuals are closely in line with previous findings.24 The spikes in the
histogram indicate the commonly observed tendency for subjects to switch at prominent
numbers. The modest spike for the most extreme degree of risk lovingness, in which sub-
jects choose the lottery over a safe payment of 190, could reflect some minor censoring,
such that a few subjects would prefer the lottery over even higher safe payments. We
verify below that our results are robust to using smoothed measures of risk aversion that
23 While a t-test rejects the null hypothesis that the mean response is identical in the two samples, wecannot reject the null hypothesis that the answers to the survey risk questions in the two samples havethe same distribution on basis of a Kolmogorov-Smirnov-test. Furthermore, an interval regressionusing the specification in column (1) of Table 1 shows that determinants of responses to the generalrisk question are very similar across the two samples. For example, we cannot reject the null that thecoefficients for gender, age, and height observed in the field experiment are the same as those observedin the SOEP survey sample.
24 For example, in a condition with comparable stakes, Holt and Laury (2002) observe 81 percent riskaverse, 13 percent risk neutral, and 6 percent risk loving. They use a similar choice-table procedure toobtain incentive compatible measures of risk preference, where switching rows categorize individualsinto different intervals of risk aversion or risk-lovingness, although they vary the probabilities insteadof the safe payment. See also Harrison et al., 2007, who use a similar measure and find a similardistribution of types in a representative sample of Danes.
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lump together switching rows into broader categories, and to using various estimation
techniques that correct for potential censoring.
Our main interest in this section is whether survey data can predict actual risk taking
behavior in the lottery experiment. In other words, we want to study whether greater
willingness to take risks in the general risk question maps into a greater willingness to
take risks in the lottery experiment. A first indication that this is indeed the case is
given by the lower panel of Figure 5. The figure shows a scatter plot where the average
certainty equivalents observed in the experiment, i.e., the average of the smallest safe
options that the corresponding subjects preferred over the lottery, are plotted against the
survey answers. The figure reveals a clearly positive relation.25
To test the predictive power of the general risk question more rigorously, we ran the
regressions reported in the lower panel of Table 2. In the first model, we simply regress
the value of the safe option at the switching point on answers given to the general risk
question. The coefficient on general risk is positive and significant at any conventional
level, indicating that the answers given in the survey do predict actual risk taking behavior.
To check robustness, we add controls in columns (2) and (3), which are essentially the
same as the controls in Table 1. Specifically, controls in column (2) include gender, age,
and height, and in Column (3) we control for many additional individual characteristics,
as in Table A.1 in the Appendix. The general risk coefficient becomes somewhat smaller
but stays significant at the one percent level. These results are also robust to alternative
estimation approaches that use a smoothed version of the dependent variable, or adjust
for potential censoring of the dependent variable.26 As an additional robustness check, we
25 For the calculation of the average value of the switching point we set the value of the safe option equalto 200 for the 31 participants who always prefer the lottery.
26 We checked robustness to combining switching row information into six broader categories, each con-taining one of the prominent number spikes. We find that the general risk question is still a highlysignificant predictor of willingness to take risks in this case, confirming that prominent number effectsdo not explain the results. Using interval regression estimation, which is a modified version of theTobit that accounts for potential left and right censoring of the dependent variable, we find essentiallyidentical results to those in Table 2. This is not surprising given that the experiment measures riskrisk preference with a relatively fine grid and thus minimizes potential censoring problems. As analternative approach we also estimated a Cox proportional hazard model, which accounts for the factthat individuals cannot switch beyond values of 190. In this case we find that a higher value for thegeneral risk scale has a highly significant negative impact on the hazard of switching to the safe optionin the choice table (switching later in the choice table indicates greater willingness to take risks), which
15
also estimated the same regressions as in Table 2, but weighting responses to the general
risk question in the field experiment by the population weights for responses to the general
risk question in the SOEP sample. This makes the two samples exactly comparable in
terms of the proportion of individuals choosing a given point on the general risk scale.
We find similar results, indicating that the small differences between the two samples do
not affect the generalizability of the validation results.27
In summary, the answers to the general risk attitude question predict actual be-
havior in the lottery quite well. The results are robust even controlling for a wide array
of observable characteristics, and using various estimation techniques, confirming the be-
havioral relevance of this survey measure.
4 Determinants and Stability Across Contexts
In this section we turn to the five questions that ask about willingness to take risks in the
specific contexts of car driving, financial matters, sports and leisure, health matters, and
career, respectively. We investigate the determinants of willingness to take risks in each
context, to assess whether our results so far on gender, age, height, and parental education
are robust to using alternative measures of risk attitudes. The analysis also sheds light
on the deeper question of whether there is a stable trait determining risk-taking across
different domains of life. In economics it is common to think of individuals as having
an underlying risk preference that affects willingness to take risks in all contexts. By
contrast, there is considerable controversy on this point in psychology (see Slovic, 1972a
and 1972b; Weber et al., 2002). We contribute to the discussion in several ways. In a
representative sample, we investigate whether the same factors determine risk attitudes
across contexts. We also assess the strength of the correlation of willingness to take risks
across contexts, and perform a principle component analysis to determine whether a single
principle component determines risk taking in all contexts.
again confirms the behavioral validity of the general risk measure.27 Results available upon request.
16
4.1 Determinants of Risk Attitudes in Different Contexts
Table 3 explores the determinants of individual risk attitudes in each of the five specific
contexts identified in the SOEP. For ease of comparison, the first column reports results
for the general risk question, shown previously in column (2) of Table 1. Columns (2)
to (6) show that the impact of the exogenous factors is, for the most part, qualitatively
similar across contexts.28 Women are significantly less willing to take risks than men in
all domains. This result is robust when adding the full set of controls. Gender differences
are most pronounced for risk attitudes in car driving and financial matters, and least
pronounced in the career domain. Increasing age reduces willingness to take risks in all
five domains, but has a particularly large impact in the domain of sports and leisure, and
a relatively small impact in financial matters. The table also shows that taller persons
are more willing to take risks, in all domains. The height effect is particularly strong for
car driving, sports and leisure, and career. The relationship between parental education
and risk attitudes is less consistent across domains. Father’s education has a positive and
significant impact on risk taking in all contexts, and mother’s education is important for
risk taking in sports and leisure and career. Adding additional controls to the regressions
has no impact on the qualitative results, and most effects remain similarly significant
across domains, with the main exception of parental education, which becomes somewhat
weaker. Regressions with all controls are reported in Table A.1 in the Appendix.29 In
summary, these results provide further support for the importance of gender, age, height,
and to a certain extent parental education, for determining willingness to take risks. The
qualitatively similar impact of these factors across contexts is also suggestive of a common
underlying risk preference.
28 The results are robust to estimating Ordered Probits, or OLS regressions.29 The coefficient estimates are virtually identical in specifications using age splines with knots at 30 and
60 years of age, and are available upon request.
17
4.2 Stability of Risk Attitudes Across Contexts
Mean responses for each context-specific question and the general risk question, reported
in Table 4, suggest that context matters for self-reported willingness to take risks.30 The
ranking of means in willingness to take risks, from greatest to least, is as follows: general,
career, sports and leisure, car driving, health, and financial matters. The ranking is nearly
identical for both men and women.
The next section in the table shows that risk attitudes are not perfectly correlated
across contexts, but the pairwise correlations are large, typically in the neighborhood of
0.5, and all are highly significant. This is suggestive of a stable, underlying risk trait,
but also there appears to be some sensitivity of risk attitudes to context. This could
also reflect variation in risk preference, or other factors, such as subjective beliefs.31 For
example, most people may view the typical risk in car driving as more dangerous than
the typical risk in sports, and thus to state a relatively lower willingness to take risks in
car driving.32
Principal components analysis using the general risk question and the five domain-
specific questions tells a similar story. About 60 percent of the variation in individual
risk attitudes is explained by one principal component, consistent with the existence of a
single underlying trait determining willingness to take risks.33 All of the questions capture
this underlying trait to some extent.34 Nevertheless, each of the other five components
30 The different number of observations across domains reflects different response rates. These differencesmay arise because individuals feel certain questions do not apply to them, e.g., a 90-year-old withouta driver’s license is free to leave blank the question about taking risks while driving a car.
31 Measurement error could also contribute to a less than perfect correlation across questions.32 Evidence from psychology suggests that risk perceptions may vary across individuals and contexts.
For example, women may perceive dangerous events (nuclear war, industrial hazards, environmentaldegradation, health problems due to alcohol abuse) as more likely to occur, in conditions where objec-tive probabilities are difficult to determine (Silverman and Kumka, 1987; Stallen and Thomas, 1988;Flynn et al., 1994; Spigner et al., 1993). Differences in risk perception could indicate that beliefs areformed based on systematically different information sets. Alternatively, biases due to emotion (e.g.,fear or dread), or overconfidence, could play a role in explaining different risk perceptions (Slovic, 1999;Loewenstein et al., 2001).
33 The eigenvalue associated with this component equals 3.61 while the eigenvalues associated with allother components are smaller than 0.57. When only one component is retained, none of the off-diagonalelements of the residual correlation matrix exceeds |0.11|.
34 The factor loadings for the different risk questions on the principal component are 0.78 (general), 0.76(car driving), 0.74 (financial matters), 0.80 (sports and leisure), 0.81 (career), and 0.75 (health).
18
explains at least five percent of the variation. This again suggests that the individual
measures do capture some additional, context-specific content, as well as capturing a
trait that is common across contexts.
5 Survey Risk Measures and Risky Behaviors
The last part of our analysis returns to the issue of behavioral validity. Previously we
demonstrated the ability of the general risk measure to predict real-stakes lottery choices
in a field experiment. In this section we study a broader range of risky behaviors, en-
compassing a variety of important economic and social contexts, and test the validity
of all six risk measures. In so doing, we hope to answer three questions. First, are the
survey instruments useful for explaining and predicting risky behavior, in terms of both
statistical and economic significance. Secondly, can the individual risk measures explain
risky behaviors in multiple contexts? Third, how do the different risk measures compare
in terms of explanatory and predictive power? In particular, how does the general risk
question as global measure of risk attitudes perform in comparison to context-specific
measures in a particular context? For example, is smoking best predicted by a health
related risk question or is it equally well explained by the general risk? The answer to
these questions is of obvious importance from both a methodological and a practical point
of view.
To address our questions we use a collection of behaviors reported by the SOEP
participants. We choose behaviors that span the different contexts identified by the five
domain-specific questions: portfolio choices (financial context), participation in sports
(sports and leisure), self-employment (career), smoking (health), and driving offenses (car
driving). All of these risky behaviors are measured as binary variables and are displayed
in Table 5, with the exception of traffic offenses, which are analyzed separately at the
end of the section. As a proxy for portfolio choice we use information about household
stock holdings. The variable “Investment in Stocks,” shown in column (1), is equal to 1 if
at least one household member holds stocks, shares, or stock options and zero otherwise.
19
Since the question about stock holdings is typically answered by the household head,
we use observations on risk preferences of household heads in column (1) only. In the
context of sports, the variable “Active Sports” takes a value of 1 if an individual actively
participates in any sports (at least once per month). The variable “Self-Employment”
is a binary variable equal to 1 if an individual is self-employed and zero otherwise. To
study risk-taking behavior in the domain of health, we use information about whether
the SOEP participant smokes or not. The corresponding variable is equal to 1 if the
respondent smokes.
Each reported coefficient estimate shown in Table 5 is based on a separate regres-
sion of the respective behavior on this particular risk measure and a set of controls.35
For these regressions we use standardized versions of the risk measures, to aid compari-
son of coefficients across regressions. The regressions are Probit models, and coefficients
are marginal effects showing the impact of a one standard deviation change in the cor-
responding measure of willingness to take risks. We report the standard errors of the
coefficients in brackets, and the log likelihoods in parentheses. For example, the three
entries in the upper left corner in column (1) say that the willingness to take risks in
general is significantly correlated with investments in stocks, the marginal effect being
0.029. The standard error of the coefficient is 0.006 and the log likelihood for this regres-
sion is -3,993. Coefficient estimates for the controls are not reported but are available on
request. Each column in the table also reports the unconditional probability of observing
the corresponding risky behavior.
Several important observations can be made from Table 5. First, all measures are
significant explanatory variables for at least some of the behaviors, providing further
confirmation of their behavioral validity. The marginal effects are also sizeable relative
to the unconditional probabilities, showing the economic significance of the risk attitude
measures. Second, the general risk question is significant in all contexts, with relatively
35 In every regression the controls include gender, age, height, and parental education, as in column (2)of Table 1, but also log household wealth, log household debt, and the log of current gross monthlyhousehold income. One exception is the regression for stock holdings, where we also control for thenumber of household members older than 18, because the likelihood that at least one person in thehousehold holds risky assets is increasing with household size.
20
large coefficients and goodness of fit. Third, each context-specific risk question explains
behavior in its respective context, and is typically the strongest risk measure for this
context. Again, this can been seen by comparing the size of the marginal effects and the
log likelihoods of the different regressions.
Investment in stocks, shown in column (1), is positively correlated with several risk
measures, as expected given the relative riskiness of this kind of financial investment.
The best predictor is the context-specific risk measure on “Financial Matters”. The log
likelihood for this regression is larger than for any other regression based on a differ-
ent risk measure, and the marginal effect is also larger. Notably, the marginal effect is
economically significant: a one standard deviation increase in willingness to take risks
in financial matters is associated with a 34 percent increase in the probability of hold-
ing stocks (relative to the unconditional probability). This implies that the exogenous
determinants of risk attitudes in the financial context, shown in Table 3, are also quan-
titatively important, through the channel of changing risk attitudes. For example, the
gender coefficient in column (3) of Table 3 was -0.77. Given that the standard deviation
of willingness to take risks in financial matters is 2.225 (see Table 4), this is a decrease of
about a third of a standard deviation. The results in Table 5 then imply that a quarter
standard deviation decrease in willingness to take risks is associated with a 12 percent
decrease in the probability of holding stocks.36 Analogous calculations show that any one
of the following – 20 fewer years of age, 20 additional centimeters of height, or having two
highly-educated parents – would increases the probability of holding stocks by about 8
percent, through the indirect channel of changing willingness to take risks.37
Column (2) of Table 5 shows that being active in sports is strongly correlated with
several risk measures, but the measure of risk taking in “Sports and Leisure” is the most
important in terms of statistical and economic significance. A one standard deviation
36 Thus, gender has a sizeable indirect effect on the probability of holding stocks through the channel ofrisk attitudes. This is also sizeable relative to the direct effect of gender on holding stocks (controllingfor risk attitudes in financial matters and other characteristics). The (unreported) estimated marginaleffect for gender in Table 5, in the regression involving risk attitudes in financial matters, is -0.083.
37 The impact through the channel of the general risk attitude is smaller, in any given context, thanthrough the corresponding context-specific attitude. The total impact of a change in general riskattitudes, however, is substantial.
21
increase in willingness to take risks in the context of sports is associated with about a 22
percent increase in the probability of participating in active sports.38
In column (3) we investigate the relationship between risk attitudes and career
choice. Given the low degree of job security and high income variability associated with
self-employment, we would expect that relatively risk-averse people choose not to become
self-employed. In fact, the coefficients on the risk measures are significant and positive,
consistent with those who are more willing to take risks being more likely to choose self-
employment. Exceptions include risk attitudes in sports and leisure, which are unrelated
to being self-employed.39 Notably, willingness to take risks in career matters is the overall
best predictor of self-employment, in terms of significance, fit, and size of the marginal
effect. A one standard deviation increase in willingness to take risks in the career context
is associated with a 43 percent increase in the likelihood of being self-employed.40
In column (4) we turn to risky health behavior in the form of smoking. Willingness
to take risks in general has a strong positive impact on the propensity to smoke, but
willingness to take risks in the domain of health has an even greater impact, as indicated
by the larger marginal effect and the higher log likelihood. The case of smoking is of
particular interest given that smoking has been used as an instrument for risk attitudes,
in cases where direct measures of risk attitude were not available (e.g., Feinberg, 1977). In
light of results in column (5), however, smoking can only be considered a very imperfect
substitute for more direct measures of risk attitudes. While smoking is strongly associated
with the willingness to take risks in the health domain, it is less correlated or not correlated
at all with risk attitudes in other domains such as financial matters or sports and leisure.
The impact of a one standard deviation increase in willingness to take risks in health
38 Because gender and age affect willingness to take risks in the context of sports (s.d. = 2.613), beingfemale, or 20 years older, decreases the probability of participating in sports by about 5 or 10 percent,respectively. Through the channel of changing risk attitudes, 20 additional centimeters of heightincreases the probability of participating in sports by about 6 percent, and having two parents withthe Abitur is associated with an increase of about 7 percent.
39 For related evidence on risk aversion and entrepreneurship, see Cramer et al. (2002).40 The impact of being female, or 20 years older, that works through the channel of willingness to take
risks in career matters (s.d. = 2.71), is an 9 percent or 18 percent decrease in the likelihood of beingself-employed, respectively. 20 additional centimeters of height, or having two parents with the Abitur,increases the probability of being self-employed by 12 percent or 10 percent, respectively, through thechannel of greater willingness to take risks in career matters.
22
matters is a 20 percent increase in the probability of being a smoker.41
In additional (unreported) regressions we also tested the relative explanatory power
of the different risk measures by regressing a given behavioral outcome on all of the
measures simultaneously. The results are very similar to the ones in Table 5, in the sense
that the corresponding domain specific risk question is the best predictor of investment
in stocks, participation in sports, self-employment, and being a smoker.42
The SOEP does not provide a measure of risky behavior in the domain of car driving.
As an out-of-sample test, however, Figure 6 compares the age profile of risk attitudes in
car driving, based on the SOEP, to the age profile for driving offenses in Germany for
the year 2002 (the most recent year for which data are publicly available).43 The top and
bottom panels show separate results for men and women, respectively. There are three
different lines for driving offense rates: the lowest shows the un-weighted rates of driving
offenses by age category; the middle line shows the same rates weighted by the fraction
of individuals holding a driver’s license in each age group; the highest line shows rates of
driving offenses weighted by automobile usage rates in each age category.44
For both genders, the figure reveals a strong correspondence between the age profile
of risk attitudes and the age profile of driving offense rates. The larger the fraction of risk
tolerant individuals in an age group (individuals choosing one of the values 6 or higher
on the risk scale), the higher is the rate of registered driving offenses for that group. This
suggests a link between risk attitudes in the domain of car driving and actual risk taking
behavior. Notably, the relationship between traffic offenses and risk attitudes in other
domains, including risk attitudes in general (not shown), is less strong. Specifically, the
41 Through the channel of willingness to take risks in health matters (s.d. = 2.465), being female, orbeing 20 years older, reduces the probability of smoking by 6 percent, respectively. The change in riskattitudes due to 20 additional centimeters of height, or the impact of two highly educated parents onrisk attitudes increases the probability of smoking by about 3 percent, respectively.
42 We also compared the performance of the different risk measures in terms of prediction accuracy whensplitting the sample and using the estimates from one subsample to predict behavior in the othersubsample. Detailed results are available upon request
43 A driving offense is registered if authorities impose an administrative fine of at least 40 Euros, or moreserious penalties such as revoking the drivers license. For data sources see the table notes.
44 Un-weighted traffic offense rates were calculated by dividing the number of registered offenses for anage group by the total population in that group in the year 2002. For data sources see figure notes.
23
low willingness to take risks in car driving among those younger than 20, compared to
car drivers in their twenties, is a particular feature of risk attitudes in the domain of car
driving. This pattern coincides with a lower traffic offense rate for this age group compared
to that of slightly older drivers (see Figure 6), while the distributions of responses to risk
questions in other contexts do not exhibit this feature.45 This points once more to the
predictive value of context-specific risk questions.
6 Discussion
This paper contributes evidence on several open questions regarding the measurement and
nature of individual risk attitudes. Using a simple survey measure that asks people to give
a global assessment of their willingness to take risks in general, we find an economically sig-
nificant impact of gender, age, height, and parental background on individual willingness
to take risks. The behavioral validity of the survey measure is verified in complementary,
incentive compatible field experiment conducted using a representative subject pool. The
results on determinants of risk attitudes are robust to using other survey measures that
ask about risk taking in specific contexts. Risk attitudes are shown to be relatively stable
across different contexts, shedding light on a deeper question about stability of willingness
to take risks as a personal trait. All of the survey measures are shown to explain various
risky behaviors, including holding stocks, smoking, self-employment, participation in ac-
tive sports, and traffic offenses. The best all-around predictor is the general risk question.
On the other hand, asking about risk attitudes in a more specific context gives a stronger
measure for the corresponding context.
The findings on the determinants of risk attitudes have important implications.
For example, previous experimental research has documented that women are less will-
ing to sort into relatively risky, tournament compensation schemes (e.g., Niederle and
45 The legal minimum driving age in Germany is 18. A plausible explanation for the relatively lowwillingness to take risks and the relatively low rate of traffic offenses for 18 to 20 year olds is theexistence of a two-year probationary period, starting at the date when the license is obtained, duringwhich the penalties for driving offenses are particularly severe. The Federal Bureau of Motor Vehiclesand Drivers states on its website that the probationary period is intended to counteract a combinationof high-willingness to take risks and lack of experience among younger drivers.
24
Vesterlund, 2007). A gender difference in willingness to take risks could be part of the ex-
planation for this important difference in behavior. In fact, another study also finds that
women prefer less risky compensation over tournaments (Dohmen and Falk, 2006), but
also asks our general risk question. They find that lower willingness to take risks among
females explains a substantial part of the gender difference in sorting decisions. An age
profile for risk attitudes could also be important, for explaining behavior at the macroe-
conomic level: demographic changes leading to a more elderly population are predicted
to lead to a more conservative pool of investors and voters, which could substantially
influence macroeconomic performance and political outcomes, increase the resistance to
reforms, and delay necessary but potentially risky policy adjustments. A role for parental
education in shaping the risk attitudes of children highlights a potentially important role
of education policy, and has implications for understanding intergenerational correlations
in economic outcomes and social mobility. The impact of height on risk attitudes could
be relevant for explaining another important puzzle, namely the documented relationship
between height and labor market earnings.46 The transmission of height from parents to
children could even be a channel through which parents influence the risk attitudes of
children.
The results of this paper also have implications for measuring risk attitudes in self-
reported surveys. They indicate that the approach of asking people for a global assessment
of willingness to take risks in fact generates a useful all-around measure. Asking questions
that include more specific contexts produce measures that are even stronger, for that
given context. On the other hand, focusing on a single context provides less predictive
power across contexts. In light of these findings, the usual practice of only eliciting risk
attitudes in the context of hypothetical financial lotteries would be expected to have
benefits for predicting financial decisions, but be a less effective approach for providing a
summary statistic of risk attitudes across other non-financial contexts. The SOEP does
46 Persico et al. (2004) find that height in adolescence, more than height in adulthood, has an impacton wages later in life. The authors’ hypothesis is that the height effect is due to the impact of heightin adolescence on confidence and self-esteem. Our results suggest that another by-product could begreater willingness to take risks, which is a plausible channel leading to higher future wages.
25
include a hypothetical lottery question, which asks people how much of an endowment
of 100,000 Euros they would invest in an asset that doubles or returns only half of the
investment with equal probability.47 In unreported results, available upon request, we
find that answers to the hypothetical lottery are in fact strong predictors for decisions in
the financial domain, in the sense that they predict holding stocks. On the other hand,
responses to the lottery do not predict self-employment, or smoking, consistent with the
hypothesis that the more narrow context produces a measure that is less informative for
risk taking in non-financial domains. It is noteworthy that we also find similarly robust
results on the impact of gender, age, and height using the lottery measure, showing that
these findings also prevail in measures that involve financial lotteries and explicit stakes
and probabilities. Responses to the general risk question are highly correlated with choices
in the hypothetical lottery question, providing an additional indication that the general
risk question has explanatory power for choices in financial lotteries.48
Our validation of the general risk question provides a valuable instrument for future
research using survey data, where a simple and low cost measure of risk attitudes is very
often needed.49 For example, once one is confident that a measure captures risk attitudes
in a behaviorally relevant way, it is possible to test theoretical predictions regarding the
relationship between risk attitudes and a given behavior, for example whether people who
are risk averse are more or less likely to be geographically mobile. Alternatively, it may
47 To make the hypothetical lottery more realistic, the wording specifies that the return on the investmentis realized after a two-year delay. This could potentially introduce a downward bias in quantitativeestimates of the curvature of utility, if risk aversion is positively correlated with impatience. On theother hand, the measure yields extremely similar results compared to other widely used hypotheticallottery measures that do not involve such a time delay (see Guiso and Paiella, 2005). The similarity ofthe results obtained with the lottery measure and with the general risk question further suggest thatthis confound introduced by the wording is negligible.
48 In fact, if one assumes CRRA utility, responses to the general risk question can be mapped into CRRAcoefficients using a combination of individuals responses to the lottery question and individual wealthinformation. Lottery responses and wealth information imply a distribution of CRRA coefficientsmainly between 1 and 10. The mapping to the general risk question indicates that for an individual withzero wealth, choosing zero on the general risk scale is equivalent to a CRRA coefficient of approximately2.9. For a given wealth level, one additional point on the general risk scale is associated with a decreasein the CRRA coefficient of 0.381, while a larger wealth level implies a larger CRRA coefficient, holdingthe degree of risk-taking constant.
49 An analysis of the re-test reliability of the general risk question reveals that the within-person corre-lation of responses is fairly high, and virtually stable over re-test intervals ranging from three weeksup to almost two years, see Dohmen et al. (2007). This provides further evidence for the notion of astable risk trait.
26
be important to control for risk attitudes in a regression, if risk attitudes determine some
type of selection process that confounds interpretation of a different variable of interest.
We have taken this next step in some of our own research. For example, Bonin et al.
(2007) use the general risk question, and show that individuals who are willing to take
risks sort into occupations with higher cross-sectional earnings risk. Jaeger et al. (2007)
show that willingness to take risks has a significant impact on geographic mobility. Two
other recent papers have also taken advantage of the experimentally-validated general
risk question. Grund and Sliwka (2006) find support for the theoretical prediction that
risk attitudes determine sorting into performance pay jobs, and Caliendo et al. (2007)
show that willingness to take risks increases the probability that an individual becomes
an entrepreneur in the future. We believe that these studies are the tip of the iceberg, in
terms of applications for validated survey measures of risk attitudes.
27
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32
Tables
Tab
le1:
Pri
mar
yD
eter
min
ants
ofG
ener
alR
isk
Att
itud
es
Dep
ende
ntva
riab
le:
will
ingn
ess
tota
keri
sks
inge
nera
l(1
)(2
)(3
)(4
)(5
)(6
)
Fem
ale
-0.5
91**
*-0
.613
***
-0.6
14**
*-0
.618
***
-0.6
05**
*-0
.577
***
[0.0
43]
[0.0
46]
[0.0
49]
[0.0
46]
[0.0
47]
[0.0
56]
Age
(in
year
s)-0
.035
***
-0.0
34**
*-0
.034
***
-0.0
28**
*-0
.027
***
-0.0
18**
*[0
.001
][0
.001
][0
.001
][0
.001
][0
.001
][0
.003
]H
eigh
t(i
ncm
)0.
030*
**0.
029*
**0.
027*
**0.
026*
**0.
027*
**0.
015*
**[0
.003
][0
.003
][0
.003
][0
.003
][0
.003
][0
.003
]A
bitu
rm
othe
r0.
241*
**0.
244*
**0.
247*
**0.
281*
**0.
172*
*[0
.071
][0
.078
][0
.071
][0
.074
][0
.085
]A
bitu
rfa
ther
0.28
6***
0.25
6***
0.23
4***
0.24
0***
0.02
9[0
.054
][0
.060
][0
.054
][0
.056
][0
.064
]Log
(hou
seho
ldw
ealt
hin
2002
)N
oN
oY
esN
oN
oY
esLog
(hou
seho
ldin
com
e20
03)
No
No
No
Yes
No
No
Log
(hou
seho
ldin
com
e20
04)
No
No
No
No
Yes
Yes
Oth
erco
ntro
lsN
oN
oN
oN
oN
oY
es
log
sigm
a0.
85**
*0.
84**
*0.
84**
*0.
84**
*0.
83**
*0.
81**
*[0
.005
][0
.006
][0
.006
][0
.006
][0
.006
][0
.007
]Log
pseu
do-lik
elih
ood
-47,
456
-42,
323
-36,
829
-42,
240
-39,
052
-31,
606
Obs
erva
tion
s21
785
1946
316
948
1946
317
998
1476
6
Inte
rval
regr
essi
onco
effici
ent
esti
mat
es.
The
depe
nden
tva
riab
leis
am
easu
refo
rge
nera
lri
skat
titu
des
mea
sure
don
asc
ale
from
0to
10,
whe
re0
indi
cate
s“n
otat
all
will
ing
tota
keri
sks”
and
10in
dica
tes
“ver
yw
illin
gto
take
risk
s”.
The
Abi
tur
exam
isco
mpl
eted
atth
een
dof
univ
ersi
ty-t
rack
high
-sch
ools
inG
erm
any;
pass
ing
the
exam
isa
pre-
requ
isit
efo
rat
tend
ing
univ
ersi
tyor
ate
chni
cal
colle
ge(w
ein
clud
eFa
chab
itur
,th
eex
amfo
rte
chni
cal
colle
ge,
inth
ein
dica
tor
for
Abi
tur.
)T
heot
her
cont
rols
inco
lum
n(9
)in
clud
esde
mog
raph
ic,
prof
essi
onal
and
othe
rse
lf-re
port
edin
form
atio
n.W
ealt
han
din
com
eco
ntro
lsar
ein
logs
.In
form
atio
nab
out
indi
vidu
alw
ealt
his
take
nfr
omth
e20
02w
ave
ofth
eSO
EP,
whi
chco
ntai
nsde
taile
din
form
atio
non
diffe
rent
asse
tsan
dpr
oper
tyva
lues
.H
ouse
hold
wea
lth
isco
nstr
ucte
dby
sum
min
gth
ew
ealt
hin
form
atio
nof
all
indi
vidu
als
inth
eho
useh
old.
Log
ged
abso
lute
valu
esof
nega
tive
wea
lth
are
adde
das
ase
para
teco
ntro
lvar
iabl
ein
the
resp
ecti
vesp
ecifi
cati
ons.
Form
ore
deta
iled
info
rmat
ion
onw
ealt
han
din
com
eva
riab
les
see
note
sto
Tab
leA
.1in
the
App
endi
x.A
llsp
ecifi
cati
ons
incl
ude
aco
nsta
nt.
Rob
ust
stan
dard
erro
rsin
brac
kets
allo
wfo
rcl
uste
ring
atth
eho
useh
old
leve
l;**
*,**
,*
indi
cate
sign
ifica
nce
at1-
,5-
,an
d10
-per
cent
leve
l,re
spec
tive
ly.
33
Tab
le2:
Val
idat
ion
ofSu
rvey
Ris
kM
easu
rein
aFie
ldE
xper
imen
t
Subje
cts
inex
peri
men
tSO
EP
resp
onde
nts
Mea
nst
d.de
v.M
edia
nM
ean
std.
dev.
Med
ian
Frac
tion
fem
ale
0.52
70.
500.
519
0.50
Age
(in
year
s)47
.77
18.4
047
47.1
717
.43
46H
eigh
t(i
ncm
)17
1.73
9.09
172
171.
399.
2717
0
Gen
eral
risk
atti
tude
(sur
vey
resp
onse
)4.
762.
545
4.42
2.38
5
Obs
erva
tion
s45
045
045
021
,875
21,8
7521
,875
(a)
Com
pari
son
ofSO
EP
and
Exp
erim
enta
lSa
mpl
e
Dep
ende
ntva
riab
le:
valu
eof
safe
opti
onat
swit
chpo
int
(1)
(2)
(3)
Will
ingn
ess
tota
keri
skin
gene
ral
5.61
4***
4.44
7***
4.01
0***
[1.0
82]
[1.1
09]
[1.4
46]
Con
trol
sfo
rge
nder
,ag
e,he
ight
No
Yes
Yes
Oth
erco
ntro
lsN
oN
oY
es
Con
stan
t57
.097
***
-68.
309
-33.
005
[5.8
36]
[70.
621]
[127
.048
]
R-s
quar
ed0.
060.
090.
25O
bser
vati
ons
450
450
313
(b)
Pre
dict
ing
Lot
tery
Cho
ices
wit
hSu
rvey
Mea
sure
Ris
kA
ttit
udes
OLS
esti
mat
es.
The
depe
nden
tva
riab
leis
the
valu
eof
the
safe
opti
onat
the
swit
chin
gpo
int.
Oth
erco
ntro
lsin
clud
eco
ntro
lsfo
rm
arit
alst
a-tu
s,nu
mbe
rof
depe
nden
tch
ildre
nun
der
16,l
ived
inG
DR
in19
89,l
ived
Abr
oad
in19
89,
loca
tion
in19
89m
issi
ng,
nati
onal
ity,
stud
ent,
educ
a-ti
onal
achi
evem
ent,
dum
mie
sfo
roc
cupa
tion
alle
vel
wit
hin
publ
ican
dpr
ivat
ese
ctor
(as
inTab
leA
.1)
inth
eA
ppen
dix)
,he
alth
stat
us,
body
wei
ght,
net
hous
ehol
din
com
e,sm
oker
,an
dlif
esa
tisf
acti
on,
com
pare
also
Tab
leA
.1.
Rob
ust
stan
dard
erro
rsin
brac
kets
;**
*,**
,*
indi
cate
sign
ifica
nce
at1-
,5-
,an
d10
-per
cent
leve
l,re
spec
tive
ly.
34
Tab
le3:
Pri
mar
yD
eter
min
ants
ofR
isk
Att
itud
esin
Diff
eren
tD
omai
nsof
Life
Dep
ende
ntva
riab
le:
will
ingn
ess
tota
keri
sks
in:
Gen
eral
Car
Fin
anci
alSp
orts
/C
aree
rH
ealt
hdr
ivin
gm
atte
rsle
isur
e(1
)(2
)(3
)(4
)(5
)(6
)
Fem
ale
-0.6
11**
*-1
.071
***
-0.7
70**
*-0
.629
***
-0.5
38**
*-0
.671
***
[0.0
46]
[0.0
59]
[0.0
52]
[0.0
53]
[0.0
60]
[0.0
55]
Age
(in
year
s)-0
.034
***
-0.0
50**
*-0
.025
***
-0.0
63**
*-0
.059
***
-0.0
36**
*[0
.001
][0
.002
][0
.001
][0
.001
][0
.002
][0
.001
]H
eigh
t(i
ncm
)0.
029*
**0.
033*
**0.
029*
**0.
034*
**0.
038*
**0.
018*
**[0
.003
][0
.004
][0
.003
][0
.003
][0
.004
][0
.003
]ab
itur
mot
her
0.24
6***
0.04
30.
137
0.26
8***
0.28
9***
0.09
[0.0
71]
[0.0
95]
[0.0
86]
[0.0
85]
[0.0
93]
[0.0
91]
abitur
fath
er0.
287*
**0.
143*
*0.
396*
**0.
558*
**0.
347*
**0.
217*
**[0
.054
][0
.070
][0
.063
][0
.064
][0
.074
][0
.070
]
Log
sigm
a0.
84**
*1.
03**
*0.
95**
*0.
97**
*1.
07**
*1.
01**
*[0
.006
][0
.007
][0
.007
][0
.006
][0
.007
][0
.007
]Log
pseu
do-lik
elih
ood
-42,
311
-37,
716
-37,
736
-40,
649
-38,
248
-40,
718
Obs
erva
tion
s19
,463
18,3
3719
,297
19,2
1117
,707
1945
6
Inte
rval
regr
essi
onco
effici
ent
esti
mat
es.
The
depe
nden
tva
riab
leis
am
easu
refo
rri
skat
titu
des
inth
ere
spec
tive
dom
ain
mea
sure
don
asc
ale
from
0to
10,w
here
0in
dica
tes
“not
atal
lw
illin
gto
take
risk
s”an
d10
indi
cate
s“v
ery
will
ing
tota
keri
sks”
.T
heA
bitu
rex
amis
com
plet
edat
the
end
ofun
iver
sity
-tra
ckhi
gh-s
choo
lsin
Ger
man
y;pa
ssin
gth
eex
amis
apr
e-re
quis
ite
for
atte
ndin
gun
iver
sity
ora
tech
nica
lco
llege
(we
incl
ude
Fach
abitur
,th
eex
amfo
rte
chni
cal
colle
ge,
inth
ein
dica
tor
for
Abi
tur.
)A
llsp
ecifi
cati
ons
incl
ude
aco
nsta
nt.
Rob
ust
stan
dard
erro
rsin
brac
kets
allo
wfo
rcl
uste
ring
atth
eho
useh
old
leve
l;**
*,**
,*in
dica
tesi
gnifi
canc
eat
1-,5
-,an
d10
-per
cent
leve
l,re
spec
tive
ly.
35
Tab
le4:
Cor
rela
tion
sB
etw
een
Ris
kA
ttit
udes
inD
iffer
ent
Con
text
s
Gen
eral
Car
Fin
anci
alSp
orts
/C
aree
rH
ealt
hdr
ivin
gm
atte
rsle
isur
eM
ean
4.42
02.
927
2.40
63.
486
3.60
52.
934
Stan
dard
Dev
iati
on2.
379
2.53
52.
225
2.61
32.
708
2.46
5M
ean
(men
)4.
909
3.52
32.
882
3.96
14.
039
3.31
8M
ean
(wom
en)
3.96
72.
346
1.96
33.
044
3.19
02.
580
Gen
eral
1.00
00C
ardr
ivin
g0.
4891
1.00
00Fin
anci
alm
atte
rs0.
5036
0.51
901.
0000
Spor
ts/l
eisu
re0.
5595
0.54
260.
4992
1.00
00C
aree
r0.
6088
0.50
700.
4978
0.60
331.
0000
Hea
lth
0.47
680.
5041
0.45
640.
5205
0.53
111.
0000
Obs
erva
tion
s21
,877
20,6
0021
,687
21,5
7019
,898
21,8
64
Corr
elati
ons
are
base
don
indiv
iduals
’ri
skatt
itudes
inea
chco
nte
xt,
report
edon
an
11-p
oin
tsc
ale
.C
hoosi
ng
0in
dic
ate
s“not
at
all
willing
take
risk
s”and
choosi
ng
10
indic
ate
s“ver
yw
illing
tota
ke
risk
s”.
Corr
elati
ons
base
don
19,0
43
obse
rvati
ons
wit
hre
sponse
sto
all
risk
ques
tions.
36
Table 5: The Relative Ability of Alternative Measures of Risk Attitudes to Explain Risky
Behaviors
Dependent variable: Investment Active sports Self-employed Smokingin stocks
(1)† (2) (3)‡ (4)
Willingness to take risks in 0.029*** 0.061*** 0.024*** 0.037***general [0.006] [0.005] [0.003] [0.004]
(-3,993) (-7,426) (-2,599) (-7,772)Willingness to take risks in
Car driving 0.024*** 0.037*** 0.007** 0.018***[0.006] [0.005] [0.003] [0.004](-3,996) (-7,489) (-2,636) (-7,801)
Financial matters 0.117*** 0.063*** 0.013*** -0.003[0.006] [0.005] [0.003] [0.004](-3,799) (-7,425) (-2,626) (-7,810)
Sports and leisure 0.052*** 0.143*** 0.003 0.002[0.006] [0.005] [0.003] [0.005](-3,969) (-7,075) (-2,638) (-7,810)
Career 0.035*** 0.065*** 0.036*** 0.025***[0.006] [0.005] [0.003] [0.004](-3,987) (-7,419) (-2,539) (-7,793)
Health 0.023*** 0.029*** 0.012*** 0.060***[0.006] [0.005] [0.003] [0.004](-3,997) (-7,499) (-2,628) (-7,704)
Observations 7,345 13,520 9,897 13,571Unconditional probability(dependent variable =1) 0.341 0.662 0.084 0.294
Dependent variables in all columns are binary variables. Willingness to take risks is measured on on a scalefrom 0 to 10, where 0 indicates “not at all willing to take risks” and 10 indicates “very willing to take risks”.All risk measures are standardized. Reported coefficients are Probit marginal effects estimates, evaluatedat the means of independent variables. Each coefficient estimate is based on a separate regression of therespective dependent variable on this particular risk measure and a set of controls, whose coefficient estimatesare not reported. This set includes the same controls for gender, age, height, and parental education as inTable 1, as well as controls for log household wealth, log household debt, and the log of current grossmonthly household income in every regression. Additional controls: † Number of Adults in household. ‡Sample excludes individuals that are older than 66 years, or retired, or non-participating in the labor market.All specifications include a constant. Robust standard errors that allow for clustering at the household levelare reported in brackets below the coefficient estimates. Values of the log pseudo-likelihood of the respectiveregression model are reported in parentheses; ***, **, * indicate significance at 1-, 5-, and 10-percent level,respectively.
37
Figures Figure 1: Willingness to Take Risks in General
0.0
5.1
.15
.2F
ract
ion
0 2 4 6 8 10Responses to General Risk Question
(0=not at all willing; 10=very willing)
All Respondents − SOEP 2004
−.0
6−
.04
−.0
20
.02
.04
Diff
eren
ce in
Fra
ctio
n
0 2 4 6 8 10Responses to General Risk Question
(0=not at all willing; 10=very willing)
Gender Differences
Notes: The top panel shows a histogram of responses to the question about willingnessto take risks “in general”, measured on an eleven-point scale. The bottom panel showsthe difference between the fraction of females and the fraction of males choosing eachresponse category. A positive difference for a given category indicates that relativelymore females choose that category.
38
Figure 2: Willingness to Take Risks in General, by Age and Gender
0.2
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20 30 40 50 60 70 80 90Age in Years
High Willingness to Take Risks 10 9 8 7 6Low Willingness to Take Risks 4 3 2 1 0
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High Willingness to Take Risks 10 9 8 7 6Low Willingness to Take Risks 4 3 2 1 0
Women
Notes: Upper and lower panels show separate results for men and women. Theshaded bands indicate the proportion of individuals at each age who choose a givenvalue on the 11-point scale for the general risk question. Starting at the bottom of agiven panel, the darkest shade shows the proportion choosing 0, indicating “not at allwilling to take risks”. Progressively lighter shades correspond to choices 1 to 4, andthe white band corresponds to 5. Above the white band, progressively darker shadesindicate choices of 6 and higher, up to 10 which indicates “very willing to take risks”.
39
Figure 3: Willingness to Take Risks in General, by Parental Education
0.0
5.1
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ract
ion
0 2 4 6 8 10Response to General Risk Question
(0=not at all willing; 10=very willing)
Father’s education: Abitur not completed
0.0
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ion
0 2 4 6 8 10Response to General Risk Question
(0=not at all willing; 10=very willing)
Mother’s education: Abitur not completed
0.0
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0 2 4 6 8 10Response to General Risk Question
(0=not at all willing; 10=very willing)
Father’s education: Abitur completed
0.0
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ion
0 2 4 6 8 10Response to General Risk Question
(0=not at all willing; 10=very willing)
Mother’s education: Abitur completed
Notes: Each panel shows, for the indicated sub-sample, the histogram of responses to the question aboutwillingness to take risks “in general”, measured on an eleven-point scale. The Abitur exam is completedat the end of university-track high-schools in Germany; passing the exam is a pre-requisite for attendinguniversity or a technical college (we include Fachabitur, the exam for technical college, in the indicator forAbitur.)
40
Figure 4: Willingness to Take Risks in General, by Height and Gender
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150 155 160 165 170 175 180 185 190 195Height in cm
High Willingness to Take Risks 10 9 8 7 6Low Willingness to Take Risks 4 3 2 1 0
Men
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150 155 160 165 170 175 180 185 190 195Height in cm
High Willingness to Take Risks 10 9 8 7 6Low Willingness to Take Risks 4 3 2 1 0
Women
Notes: Each shaded band gives the fraction of individuals choosing a particular num-ber on the eleven-point response scale for the question about willingness to take risks“in general.” The dark band at the bottom corresponds to a choice of zero, “not atall willing”, with progressively lighter shades indicating 1 through 4. The white bandis the fraction choosing 5, and the progressively darker shades represent fractionschoosing values above 6, up to 10 which indicates “very willing”. In order to dealwith small cell size, we pooled men taller than 195 cm with those being 195 cm tall,and men smaller than 160 cm with those being 160 cm tall. Similarly, we pooledwomen smaller than 150 cm with women who are 150 cm tall and women taller than185 cm with those of being 185 cm tall.
41
Figure 5: Responses to General Risk Question and Lottery Choices in Field Experiment
0.0
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0 1 2 3 4 5 6 7 8 9 10Response to General Risk Question
(0=not at all willing; 10=very willing)
0.0
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0 50 100 150 200Safe Value at Switch Point
Choices of Subjects in Experiment
6080
100
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Ave
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alue
at S
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oint
0 1 2 3 4 5 6 7 8 9 10Response to General Risk Question
Fitted Line
(0=not at all willing; 10=very willing)
Notes: The upper panel of shows the distribution of responses of subjects inthe field experiment to the SOEP survey question about willingness to takerisks “in general”. The middle panel shows the distribution of the safe optionat the switching points in the field experiment. The lower panel shows theaverage value of the safe option at the switching point of respondents in agiven response category.
42
Figure 6: Registered Traffic Offenses and Willingness to Take Risks in Driving
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20 30 40 50 60 70 80 90Age in Years
Willingness to Take Risks 10 9 8 7 6 Unweighted Offense Rate Weighted Offense Rate use lic.
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20 30 40 50 60 70 80 90Age in Years
Willingness to Take Risks 10 9 8 7 6 Unweighted Offense Rate Weighted Offense Rate use lic.
Women
Notes: Upper and lower panels report separate results for men and women. Each shaded bandgives the fraction of individuals of a birth year cohort choosing a number between 6 and ten onthe eleven-point response scale for the question about willingness to take risks while driving a car.Progressively darker shades represent fractions choosing 6 through 10. The lines plot the rates ofregistered traffic offenses in the year 2002, the latest year for which data is available at the GermanFederal Bureau of Motor Vehicles and Drivers (Kraftfahrtbundesamt). Weighted offense rates arecalculated based on information about car usage and driver’s license ownership.
Data Sources: Risk attitudes are obtained from the SOEP, own calculations. The numberof entries in the German Central Register of Traffic Offenders in the year 2002 (aggregatedby gender age groups) are obtained from the Federal Bureau of Motor Vehicles and Driversat www.kba.de/Abt3 neu/Verkehrsverstoesse/Personen im VZR/a Haupt Personen im VZR.htm Dataon population by gender cohorts in 2002 was provided by the Federal Statistical Office. Fi-nally, usage weights and license weights are calculated based on information contained in thestudy “Mobilitat in Deutschland” (Mobility in Germany) which was authorized by the GermanInstitute for Economic Research (DIW) and conducted in 2002. For further information visitwww.kontiv2002.de/engl/index.htm.
43
A Appendix
Table A.1: Determinants of Risk Attitudes
Dependent Variable:Willingness to take Risks in the Domain of:
General Car Financial Sports & Career HealthDriving Matters Leisure
(1) (2) (3) (4) (5) (6)Female -0.577*** -1.162*** -0.883*** -0.801*** -0.576*** -0.502***
[0.056] [0.071] [0.063] [0.065] [0.074] [0.067]Age (in years) -0.018*** -0.047*** -0.022*** -0.048*** -0.040*** -0.023***
[0.003] [0.003] [0.003] [0.003] [0.004] [0.003]Height (in cm) 0.015*** 0.017*** 0.010*** 0.019*** 0.018*** -0.001
[0.003] [0.004] [0.004] [0.004] [0.004] [0.004]Abitur Mother 0.172** 0.066 0.091 0.123 0.106 0.031
[0.085] [0.112] [0.100] [0.104] [0.109] [0.108]Abitur Father 0.029 0.004 0.019 0.188** -0.027 0.021
[0.064] [0.084] [0.073] [0.077] [0.088] [0.081]Married -0.154** -0.059 -0.168* -0.246*** -0.200** -0.287***
[0.071] [0.095] [0.086] [0.086] [0.096] [0.089]Divorced 0.074 0.059 -0.099 -0.069 0.109 -0.192
[0.096] [0.126] [0.112] [0.112] [0.128] [0.121]Widowed -0.292** -0.258 -0.151 -0.395*** -0.347* -0.383***
[0.118] [0.159] [0.138] [0.141] [0.182] [0.142]1 Child born after 1987 -0.021 0.007 -0.021 -0.114 0.058 -0.059
[0.066] [0.087] [0.077] [0.082] [0.084] [0.082]2 Children born after 1987 -0.097 -0.078 0.023 -0.200** 0.115 0.055
[0.074] [0.095] [0.086] [0.088] [0.095] [0.089]3 Children born after 1987 -0.208 -0.328** -0.249* -0.486*** 0.003 -0.07
[0.130] [0.155] [0.146] [0.149] [0.161] [0.156]> 3 Children born after 1987 -0.132 -0.484* -0.398 -0.537 -0.435 -0.147
[0.321] [0.261] [0.298] [0.336] [0.356] [0.308]Catholic -0.120** 0.051 0.075 -0.048 -0.048 0.02
[0.054] [0.067] [0.060] [0.064] [0.073] [0.065]Other Christian -0.677*** -0.347 -0.051 -0.615*** -0.647*** -0.453**
[0.164] [0.217] [0.188] [0.188] [0.205] [0.205]Other Non-Christian -0.408*** -0.172 -0.231 -0.350* -0.325 -0.451**
[0.156] [0.189] [0.170] [0.179] [0.201] [0.180]No Religion 0.232*** 0.205*** 0.157** 0.198*** 0.202*** 0.201***
[0.057] [0.074] [0.067] [0.069] [0.076] [0.069]Missing Religion 0.173 -0.103 -0.035 0.03 0.178 0.247
[0.144] [0.204] [0.172] [0.183] [0.197] [0.188]Lived in GDR in 1989 0.203* 0.062 0.035 -0.037 0.034 0.049
[0.115] [0.146] [0.125] [0.136] [0.146] [0.137]Lived abroad in 1989 -0.175 -0.380** -0.105 -0.560*** -0.188 -0.155
[0.138] [0.165] [0.155] [0.158] [0.175] [0.154]Residence in 1989 missing -0.683 -0.382 -0.486 -0.616 -0.5 -0.752
[0.566] [0.514] [0.556] [0.567] [0.641] [0.604]Lives in East Germany in 2004 0.062 -0.028 0.039 0.129 0.420*** 0.19
[0.113] [0.144] [0.123] [0.134] [0.145] [0.135]German Nationality -0.026 0.029 0.208 -0.144 -0.134 0.275**
[0.116] [0.142] [0.130] [0.130] [0.148] [0.133]School Degree 0.527*** 0.292 0.12 0.632*** 0.666*** 0.274
[0.172] [0.225] [0.206] [0.208] [0.228] [0.201]Abitur 0.269*** 0.272*** 0.454*** 0.423*** 0.498*** 0.340***
[0.052] [0.066] [0.060] [0.062] [0.071] [0.064]Subjective Health Status -0.100*** -0.066** -0.036 -0.212*** -0.05 0.008
[0.027] [0.033] [0.030] [0.031] [0.036] [0.032]Smoker 0.422*** 0.142** -0.071 0.049 0.300*** 0.796***
[0.045] [0.058] [0.053] [0.054] [0.059] [0.056]Weight (in kg) 0.0004 0.0002 -0.004** -0.011*** -0.006*** 0.009***
[0.002] [0.002] [0.002] [0.002] [0.002] [0.002]Enrolled in School 0.476 -0.802* -0.085 0.695** 0.144 0.51
[0.307] [0.478] [0.415] [0.344] [0.427] [0.330]Enrolled in Continuing Professional 0.285 -1.051*** -0.445 0.118 0.148 0.174
Training [0.219] [0.333] [0.303] [0.280] [0.330] [0.304]Enrolled in College/University -0.073 -0.486** -0.534*** -0.121 -0.096 0.168
[0.191] [0.246] [0.207] [0.214] [0.227] [0.233]
44
Table A.1: continued: Determinants of Risk Attitudes
Dependent Variable:Willingness to take Risks in the Domain of:
General Car Financial Sports & Career HealthDriving Matters Leisure
(1) (2) (3) (4) (5) (6)Public Sector:
Unskilled Blue Collar -0.775** -0.922* -0.193 -0.257 -1.016** 0.455[0.352] [0.495] [0.455] [0.422] [0.475] [0.430]
Skilled Blue Collar -0.681** -0.475 -0.47 -0.502 -0.724** -0.036[0.268] [0.344] [0.334] [0.318] [0.346] [0.311]
Blue Collar Craftsman -0.134 -0.535* 0.026 0.207 -0.424 0.254[0.241] [0.307] [0.271] [0.290] [0.314] [0.309]
Blue Collar Foreman 0.19 -1.274 0.654 0.891 -1.043 -0.635[0.611] [0.906] [1.062] [1.198] [0.981] [0.894]
Blue Collar Master 0.168 0.534 0.513 1.376*** 0.285 0.554[0.670] [0.535] [0.449] [0.504] [0.612] [0.595]
Unskilled White Collar 0.175 -0.078 -0.486 -0.087 -0.142 0.58[0.252] [0.334] [0.305] [0.314] [0.325] [0.356]
Skilled White Collar 0.137 0.157 -0.015 0.098 0.043 0.33[0.203] [0.246] [0.206] [0.226] [0.236] [0.235]
White Collar Technician -0.104 0.144 0.166 0.165 -0.078 0.328*[0.141] [0.181] [0.156] [0.161] [0.170] [0.170]
White Collar Master 0.169 0.453 -3.260** 0.123 -0.914 0.84[0.725] [2.010] [1.598] [1.329] [0.683] [2.000]
Highly-Skilled White Collar -0.048 -0.335 0.327* 0.21 0.26 0.576***[0.165] [0.208] [0.188] [0.186] [0.203] [0.201]
White Collar Management 0.021 0.017 -0.159 -0.717 0.5 1.190**[0.458] [0.593] [0.610] [0.602] [0.664] [0.594]
Apprentice (technical) 0.105 -1.447** -0.772 -0.558 0.245 0.13[0.539] [0.666] [0.535] [0.576] [0.484] [0.516]
Apprentice (clerical) 0.255 -0.513 -0.202 -0.375 -0.179 0.651[0.459] [0.565] [0.482] [0.623] [0.576] [0.632]
Intern/Trainee -0.583 -0.473 0.379 -0.603 -0.235 0.052[0.601] [0.909] [0.800] [0.732] [0.696] [0.874]
Civil Servant 0.963* 0.2 0.407 1.524** 1.198 1.005[0.503] [0.741] [0.584] [0.620] [0.730] [0.845]
Civil Servant Intermediate 0.203 -0.006 0.0003 0.541** 0.407* 0.533**[0.191] [0.245] [0.235] [0.238] [0.239] [0.239]
Civil Servant High 0.034 -0.21 0.057 0.328* 0.138 0.264[0.163] [0.205] [0.183] [0.181] [0.210] [0.206]
Civil Servant Executive 0.035 -0.554** -0.039 0.165 0.139 0.244[0.186] [0.237] [0.205] [0.230] [0.237] [0.231]
Private Sector:Unskilled Blue Collar -0.365* -0.309 -0.425* -0.284 -0.542** -0.11
[0.199] [0.259] [0.221] [0.223] [0.244] [0.234]Skilled Blue Collar -0.271* -0.058 -0.314* -0.157 -0.387** 0.086
[0.143] [0.184] [0.162] [0.164] [0.171] [0.171]Blue Collar Craftsman -0.249* -0.101 -0.195 -0.206 -0.498*** 0.03
[0.141] [0.178] [0.155] [0.160] [0.168] [0.167]Blue Collar Foreman -0.068 0.047 -0.056 -0.059 0.225 0.677**
[0.242] [0.296] [0.273] [0.302] [0.294] [0.283]Blue Collar Master 0.368 0.175 -0.209 -0.202 0.594* 0.428
[0.277] [0.357] [0.328] [0.349] [0.341] [0.377]Unskilled White Collar -0.17 -0.157 -0.326 -0.28 -0.18 0.074
[0.177] [0.226] [0.199] [0.200] [0.208] [0.209]White Collar Technician 0.039 0.157 0.286** 0.115 0.292* 0.326**
[0.125] [0.161] [0.140] [0.144] [0.152] [0.153]Highly-Skilled White Collar 0.127 -0.052 0.113 0.13 0.261 0.066
[0.386] [0.517] [0.435] [0.475] [0.496] [0.516]White Collar Master 0.297** 0.289* 0.674*** 0.259* 0.706*** 0.601***
[0.135] [0.174] [0.150] [0.156] [0.166] [0.165]White Collar Management 0.779*** 0.450* 1.207*** 0.291 1.341*** 0.729***
[0.201] [0.258] [0.224] [0.228] [0.247] [0.242]Apprentice (technical) 0.330* -0.720*** -0.427* 0.168 -0.336 0.527**
[0.198] [0.265] [0.237] [0.247] [0.246] [0.234]Apprentice (clerical) 0.18 -0.802** -0.191 -0.137 -0.25 0.33
[0.253] [0.341] [0.285] [0.306] [0.303] [0.299]Intern/Trainee 0.445 -0.076 -0.197 0.125 -0.285 0.304
[0.357] [0.554] [0.474] [0.396] [0.458] [0.425]Self-employment:
Professional Services 0.726*** 0.125 0.728*** 0.448** 1.426*** 0.676***[0.174] [0.214] [0.197] [0.199] [0.221] [0.209]
Other Self-employment 0.784*** 0.161 0.536*** -0.012 1.455*** 0.677***[0.150] [0.186] [0.164] [0.174] [0.179] [0.178]
Agriculture -0.489 -0.807 -0.762* -1.005* 0.502 0.1[0.495] [0.542] [0.453] [0.592] [0.614] [0.548]
45
Table A.1: continued: Determinants of Risk Attitudes
Dependent Variable:Willingness to take Risks in the Domain of:
General Car Financial Sports & Career HealthDriving Matters Leisure
(1) (2) (3) (4) (5) (6)
Helping Family Member 0.014 -0.35 -0.249 -0.101 0.382 -0.113[0.365] [0.522] [0.559] [0.509] [0.529] [0.494]
Military/Civil Service 0.095 -0.43 -0.592 0.153 -0.554 0.246[0.309] [0.413] [0.376] [0.357] [0.354] [0.425]
No / Missing Occupation 1.203** 1.357** 0.484 1.513*** 0.783 -0.011[0.473] [0.679] [0.628] [0.580] [0.590] [0.701]
Unemployed 0.02 -0.300* -0.292* -0.128 -0.017 -0.125[0.140] [0.180] [0.157] [0.160] [0.175] [0.168]
Non-Participating -0.273** -0.577*** -0.224 -0.228 -0.772*** -0.121[0.133] [0.169] [0.146] [0.150] [0.170] [0.157]
Retired (Pension) -0.304** -0.392** -0.099 -0.128 -1.017*** -0.014[0.136] [0.175] [0.153] [0.158] [0.182] [0.164]
Log(Household Wealth in 2002) 0.021*** 0.045*** 0.064*** 0.036*** 0.029*** 0.033***[0.007] [0.009] [0.008] [0.008] [0.009] [0.008]
Log(Household Debt in 2002) 0.063*** 0.045*** 0.037** 0.050*** 0.071*** 0.045***[0.012] [0.016] [0.015] [0.014] [0.016] [0.015]
Log(Household Income 2004) 0.042*** 0.092*** 0.098*** 0.079*** 0.065*** 0.039**[0.013] [0.016] [0.015] [0.015] [0.019] [0.016]
Life Satisfaction 0.106*** 0.013 0.022 0.042** 0.014 -0.004[0.014] [0.017] [0.016] [0.016] [0.019] [0.017]
Month of Interview:January -0.251 -0.875 -1.096 -1.093* -1.412** -0.413
[0.443] [0.746] [0.782] [0.662] [0.672] [0.871]February -0.162 -0.755 -1.031 -0.91 -1.221* -0.366
[0.442] [0.745] [0.782] [0.662] [0.671] [0.871]March -0.346 -0.768 -1.059 -0.999 -1.366** -0.519
[0.443] [0.747] [0.783] [0.663] [0.673] [0.872]April -0.065 -0.678 -0.893 -0.752 -1.121* -0.208
[0.447] [0.751] [0.786] [0.666] [0.676] [0.875]May -0.476 -0.948 -1.253 -1.061 -1.341** -0.46
[0.451] [0.754] [0.788] [0.671] [0.683] [0.878]June -0.496 -0.675 -0.977 -1.031 -1.429** -0.253
[0.458] [0.760] [0.794] [0.676] [0.692] [0.881]July -0.201 -0.879 -0.997 -0.875 -1.187* -0.214
[0.459] [0.761] [0.793] [0.678] [0.690] [0.882]August -0.048 -0.85 -1.297 -1.011 -1.144 -0.55
[0.482] [0.780] [0.816] [0.696] [0.716] [0.900]September -0.083 -0.983 -0.706 -0.207 -0.527 0.092
[0.532] [0.831] [0.840] [0.752] [0.773] [0.921]Constant 0.981 1.374 0.935 2.554** 1.910* 2.115*
[0.798] [1.117] [1.085] [1.017] [1.097] [1.176]
log sigma 0.807*** 0.991*** 0.903*** 0.946*** 1.023*** 0.973***[0.007] [0.007] [0.008] [0.007] [0.008] [0.008]
Pseudo Log Likelihood -31,606 -28,178 -28,090 -30,188 -28,016 -30,297Observations 14,766 13,967 14,708 14,573 13,386 14,764
Interval regression coefficient estimates. The dependent variable is a measure for risk attitudes in therespective domain measured on a scale from 0 to 10, where 0 indicates “not at all willing to take risks”and 10 indicates “very willing to take risks”. The Abitur exam is completed at the end of university-trackhigh-schools in Germany; passing the exam is a pre-requisite for attending university or a technical college(we include Fachabitur, the exam for technical college, in the indicator for Abitur.) Information on religionis taken from the 2003 wave. The omitted category is Protestant. The category “No religion” includesthose who are not officially affiliated with any type of church. Wealth and income controls are in logs.Information about individual wealth is taken from the 2002 wave of the SOEP, which contains detailedinformation on different assets and property values, see Schafer and Schupp (2006) for details. Householdwealth is constructed by summing the wealth information of all individuals in the household, and is treatedas missing if the wealth information is missing for at least one member of the household. An exception isthe case where the individual with missing wealth participated in the survey for the first time in 2004, and isyounger than 20 years of age; in this case the missing value for the (teenage) individual’s wealth is assumedto indicate zero wealth. Results are qualitatively unchanged if we do not make this assumption. Loggedabsolute values of negative wealth are added as a separate control variable in the respective specifications.Wealth and income controls are in logs. Logged absolute values of negative wealth are added as separatecontrol. The income data for 2004 are based on answers to questions about current gross monthly incomesources at the time of the interview. We also used the net monthly income measure that is available as agenerated variable in the SOEP; the results (not reported here) are essentially the same. Robust standarderrors in brackets allow for clustering at the household level; ***, **, * indicate significance at 1-, 5-, and10-percent level, respectively.
46